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共聚焦激光扫描显微镜(CLSM)记录图像的手动分割与基于阈值分割之间的定量比较。

A quantitative comparison between manual segmentation and threshold-based segmentation of CLSM recorded images.

作者信息

Anderson Jeffrey R, Barrett Steven F

机构信息

Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA.

出版信息

Biomed Sci Instrum. 2007;43:290-5.

Abstract

Segmentation is the process of defining distinct objects in an image. Object segmentation of two-dimensional images is often accomplished by a time consuming manual process where trained persons trace a line along the boundary of the object. Significant effort has been directed towards various computer segmentation algorithms to reduce the time required to segment each object. Often the question arises as to the accuracy of the computer segmentation results. This paper makes a quantitative comparison between the segmented object from a threshold-based computer segmentation process and the manual segmentation results from a group of volunteers. This comparison is based on the fact that humans have an intuitive capability to recognize objects. The image sample used in this report is a portion of the brain of the common housefly, Musca domestica. The very small size of the object makes it impractical to compare the computer segmentation results to the actual object.

摘要

分割是定义图像中不同对象的过程。二维图像的对象分割通常通过耗时的人工过程来完成,即由经过培训的人员沿着对象边界绘制一条线。人们已经投入了大量精力来研究各种计算机分割算法,以减少分割每个对象所需的时间。计算机分割结果的准确性常常成为问题。本文对基于阈值的计算机分割过程得到的分割对象与一组志愿者的手动分割结果进行了定量比较。这种比较基于人类具有识别对象的直观能力这一事实。本报告中使用的图像样本是家蝇(Musca domestica)大脑的一部分。对象的尺寸非常小,使得将计算机分割结果与实际对象进行比较不切实际。

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